Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle
Keywords:
agriculture mechanization, 4WID electric vehicle, self-steering, model predictive controlAbstract
A model predictive control (MPC) approach based on direct yaw moment control (DYC) was proposed to realize the self-steering drive for a newly autonomous four-wheel independent-drive (4WID) agricultural electric vehicle. The front axle and rear axle of the vehicle chassis could rotate simultaneously around their respective center points and cut the turning radius in half at most through specific mechanical chassis structure design and four-wheel electrical drive. It had great potential to reduce wheel traffic damage to field crops if two rear electrical drive wheels can be controlled to follow wheel tracks of two front wheels during self-steering operation. Therefore, firstly, a two-degree-freedom dynamics model presenting this agricultural electric vehicle was constructed. Then, an MPC controller combined with DYC was applied to arrange torques from four wheels to match desired turning angles, direct yaw moments and travel speeds. The simulation results existed small steady error of steering angles below 0.22% as they were set at 5°, followed with yaw moment under 0.17% and velocity less than 1%. Finally, according to experiment results, the vehicle successfully made a working turning radius of 9.1 m with maximum error of 0.55% when desired steering angles were 5° at the speed of 1 m/s and a minimum turning radius of 1.51 m with maximum error of 6.6% when steering angles were 30° at the speed of 0.5 m/s. It verified that the 4WID agricultural electric vehicle could drive autonomously and steady with small self-steering angle error under the proposed control system and has a feasibility to reduce wheel traffic damage during driving and operation. Keywords: agriculture mechanization, 4WID electric vehicle, self-steering, model predictive control DOI: 10.25165/j.ijabe.20211402.5283 Citation: Liu H, Yan S C, Shen Y, Li C H, Zhang Y F, Hussain F. Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle. Int J Agric & Biol Eng, 2021; 14(2): 175–181.References
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[3] Zhou Q Q, Xue X Y, Qin W C, Cai C, Zhou L F. Optimization and test for structural parameters of UAV spraying rotary cup atomizer. Int J Agric & Biol Eng, 2017; 10(3): 78–86.
[4] Pei W, Lan Y B, Luo X W, Zhou Z Y, Wang Y. Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system. Int J Agric & Biol Eng, 2014; 7(2): 75–81.
[5] Singh K B, Arat M A, Taheri S. An intelligent tire based tire-road friction estimation technique and adaptive wheel slip controller for antilock brake system. Journal of Dynamic Systems, Measurement, and Control, 2013; 135(3): 31002–31002.
[6] Doumiati M, Sename O, Dugard L, Martinez-Molina J J, Gaspar P, Szabo Z. Integrated vehicle dynamics control via coordination of active front steering and rear braking. European Journal of Control, 2013; 19(2): 121–143.
[7] Zhao C, Guo L. PID controller design for second order nonlinear uncertain systems. Science China Information Sciences, 2017; 60: 022201.
[8] Wang Y, Jin Q, Zhang R. Improved fuzzy PID controller design using predictive functional control structure. ISA Transactions, 2017; 71(2): 354–363.
[9] Mahto T, Mukherjee V. Fractional order fuzzy PID controller for wind energy based hybrid power system using quasi-oppositional harmony search algorithm. IET Generation Transmission & Distribution, 2017; 11(13): 3299–3309.
[10] Fernandes H R, Garcia A P. Design and control of an active suspension system for unmanned agricultural vehicles for field operations. Biosystems Engineering, 2018; 174: 107–114.
[11] Julián S H, Rincón V J, Francisco P, Francisco A, Fernando C. Field evaluation of a self-propelled sprayer and effects of the application rate on
spray deposition and losses to the ground in greenhouse tomato crops. Pest Management Science, 2011; 67(8): 942–947.
[12] Qiu Q, Fan Z, Meng Z, Zhang Q, Cong Y, Li B, et al. Extended Ackerman Steering Principle for the coordinated movement control of a four wheel drive agricultural mobile robot. Computers and Electronics in Agriculture, 2018; 152: 40–50.
[13] Shen Y, Zhang B, Liu H, Cui Y, Hussain F, He S, et al. Design and development of a novel independent wheel torque control of 4WD electric vehicle. Mechanics, 2019; 25(3): 210–218.
[14] Cao D, Tang B, Jiang H, Yin C, Zhang D, Huang Y. Study on low-speed steering resistance torque of vehicles considering friction between tire and pavement. Applied Science, 2019; 9: 1015.
[15] Yadbantung R, Bumroongsri P. Tube-based robust output feedback MPC for constrained LTV systems with applications in chemical processes. European Journal of Control, 2019; 47: 11–19.
[16] Alamir M. Numerical investigation regarding an MPC scheme with non uniformly weighted stage cost without terminal constraints: Application to the control of a real-life cryogenic plant. IFAC Papers Online, 2018; 51(20): 418–423.
[17] Magdy G, Shabib G, Elbaset A A, Mitani Y. Frequency stabilization of renewable power systems based on MPC with application to the Egyptian Grid. IFAC Papers Online, 2018; 51(28): 280–285.
[18] Villanueva M E, Quirynen R, Diehl M, Chachuat B, Houska B. Robust MPC via min–max differential inequalities. Automatica, 2017; 77(C): 311–321.
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[24] Zhao Z, Du R. Yaw moment control strategy for four wheel side driven EV. Automatic Control & Computer Sciences, 2018; 52(1): 32–39.
[25] Ding S, Lu L, Wei X Z. Sliding mode direct yaw-moment control design for in-wheel electric vehicles. IEEE Transactions on Industrial Electronics, 2017; 64(8): 6752–6762.
[26] Kobayashi T, Katsuyama E, Sugiura H, Ono E, Yamamoto M. Direct yaw moment control and power consumption of in-wheel motor vehicle in steady-state turning. Vehicle System Dynamics, 2017; 55(1): 104–120.
[27] Guo J, Luo Y, Li K, Dai Y. Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation. Mechanical Systems & Signal Processing, 2018; 105: 183–199.
[28] Jing Z, Wong P K, Ma X, Xie Z. Chassis integrated control for active suspension, active front steering and direct yaw moment systems using hierarchical strategy. Vehicle System Dynamics, 2017; 55(1): 72–103.
[29] Yu S Y, Wang J, Wang Y, Chen H. Disturbance observer based control for four wheel steering vehicles with model reference. IEEE/CAA Journal of Automatica Sinica, 2018; 5(6): 1121–1127.
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Published
2021-04-03
How to Cite
Liu, H., Yan, S., Shen, Y., Li, C., Zhang, Y., & Hussain, F. (2021). Model predictive control system based on direct yaw moment control for 4WID self-steering agriculture vehicle. International Journal of Agricultural and Biological Engineering, 14(2), 175–181. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5283
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Information Technology, Sensors and Control Systems
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